#145 Carthage (9-3)

avg: 1172.13  •  sd: 81.55  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
247 Wisconsin-Whitewater Win 10-7 1137.88 Mar 19th Meltdown College
294 Winona State Win 10-8 768.49 Mar 19th Meltdown College
214 Wheaton (Illinois) Win 9-7 1164.83 Mar 19th Meltdown College
309 Wisconsin-Stevens Point** Win 10-2 988.87 Ignored Mar 19th Meltdown College
191 Grace Loss 9-10 856.11 Mar 19th Meltdown College
321 Minnesota-C** Win 11-3 904.01 Ignored Mar 25th Old Capitol Open
255 Toledo Win 12-5 1315.6 Mar 25th Old Capitol Open
207 Illinois State Loss 12-13 785.86 Mar 25th Old Capitol Open
226 Wisconsin-La Crosse Win 9-7 1083.5 Mar 25th Old Capitol Open
64 St. Olaf Loss 9-10 1443 Mar 26th Old Capitol Open
183 Minnesota-B Win 9-5 1540.02 Mar 26th Old Capitol Open
121 Michigan Tech Win 13-10 1621.44 Mar 26th Old Capitol Open
**Blowout Eligible


The uncertainty of the mean is equal to the standard deviation of the set of game ratings, divided by the square root of the number of games. We treated a team’s ranking as a normally distributed random variable, with the USAU ranking as the mean and the uncertainty of the ranking as the standard deviation
  1. Calculate uncertainy for USAU ranking averge
  2. Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
  3. Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
  4. Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
  5. Subtract one from each fraction for "autobids"
  6. Award remainings bids to the regions with the highest remaining fraction, subtracting one from the fraction each time a bid is awarded
There is an article on Ulitworld written by Scott Dunham and I that gives a little more context (though it probably was the thing that linked you here)